Overview

Dataset statistics

Number of variables25
Number of observations180
Missing cells248
Missing cells (%)5.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.2 KiB
Average record size in memory314.1 B

Variable types

NUM22
CAT3

Warnings

Government_Effectiveness is highly correlated with Corruption and 1 other fieldsHigh correlation
Corruption is highly correlated with Government_Effectiveness and 1 other fieldsHigh correlation
Rule_of_Law is highly correlated with Corruption and 1 other fieldsHigh correlation
Population is highly correlated with Labour Force Total and 1 other fieldsHigh correlation
Labour Force Total is highly correlated with Population and 1 other fieldsHigh correlation
Urban Population is highly correlated with Labour Force Total and 1 other fieldsHigh correlation
Fertility_Rate has 6 (3.3%) missing values Missing
Corruption has 3 (1.7%) missing values Missing
Government_Effectiveness has 3 (1.7%) missing values Missing
Rule_of_Law has 3 (1.7%) missing values Missing
Government_Healthcare_Spend has 10 (5.6%) missing values Missing
Urban_Population has 62 (34.4%) missing values Missing
Smoking_Prevalence has 42 (23.3%) missing values Missing
Tourism has 34 (18.9%) missing values Missing
Women_In_Parliament has 3 (1.7%) missing values Missing
Obesity_Rate has 4 (2.2%) missing values Missing
Year has 7 (3.9%) missing values Missing
Diabetes Prevelance % of Population 20-79 has 7 (3.9%) missing values Missing
Labour Force Total has 13 (7.2%) missing values Missing
Population has 8 (4.4%) missing values Missing
Population Aged 65 and above % of Total has 10 (5.6%) missing values Missing
Urban Population has 8 (4.4%) missing values Missing
Death Rate Per 1000 (2017) has 9 (5.0%) missing values Missing
PM2.5 air pollution, mean annual exposure (micrograms per cubic meter) (2017) has 7 (3.9%) missing values Missing
PM2.5 air pollution, population exposed to levels exceeding WHO guideline value (% of total) (2017) has 7 (3.9%) missing values Missing
df_index has unique values Unique
Country has unique values Unique
PM2.5 air pollution, population exposed to levels exceeding WHO guideline value (% of total) (2017) has 6 (3.3%) zeros Zeros

Reproduction

Analysis started2020-10-15 22:40:59.038872
Analysis finished2020-10-15 22:42:28.346997
Duration1 minute and 29.31 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20898.44444
Minimum0
Maximum41581
Zeros1
Zeros (%)0.6%
Memory size1.5 KiB
2020-10-15T23:42:28.543881image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2159.9
Q110265.5
median21063.5
Q331477.5
95-th percentile39463.1
Maximum41581
Range41581
Interquartile range (IQR)21212

Descriptive statistics

Standard deviation12136.817
Coefficient of variation (CV)0.5807521718
Kurtosis-1.214059344
Mean20898.44444
Median Absolute Deviation (MAD)10685.5
Skewness-0.00376174743
Sum3761720
Variance147302326.9
MonotocityStrictly increasing
2020-10-15T23:42:28.805226image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2241910.6%
 
3770810.6%
 
2518010.6%
 
1586810.6%
 
3903610.6%
 
2337210.6%
 
3368310.6%
 
3084310.6%
 
1851310.6%
 
750410.6%
 
391810.6%
 
1108510.6%
 
1518010.6%
 
161010.6%
 
3616910.6%
 
1184810.6%
 
2669510.6%
 
2567010.6%
 
3791310.6%
 
3923610.6%
 
2822710.6%
 
1647710.6%
 
4091510.6%
 
1254010.6%
 
2774410.6%
 
Other values (155)15586.1%
 
ValueCountFrequency (%) 
010.6%
 
28210.6%
 
49510.6%
 
77710.6%
 
99110.6%
 
119110.6%
 
139310.6%
 
161010.6%
 
189210.6%
 
217410.6%
 
ValueCountFrequency (%) 
4158110.6%
 
4137810.6%
 
4119710.6%
 
4091510.6%
 
4070810.6%
 
4050210.6%
 
4029510.6%
 
4001310.6%
 
3973110.6%
 
3944910.6%
 

Continent
Categorical

Distinct6
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Africa
54 
Europe
45 
Asia
42 
North America
23 
South America
12 
ValueCountFrequency (%) 
Africa5430.0%
 
Europe4525.0%
 
Asia4223.3%
 
North America2312.8%
 
South America126.7%
 
Oceania42.2%
 
2020-10-15T23:42:29.031010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-15T23:42:29.145445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:29.377837image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length6
Mean length6.916666667
Min length4

Overview of Unicode Properties

Unique unicode characters20
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
r15712.6%
 
a13911.2%
 
i13510.8%
 
A13110.5%
 
c937.5%
 
e846.7%
 
o806.4%
 
u574.6%
 
f544.3%
 
E453.6%
 
p453.6%
 
s423.4%
 
t352.8%
 
h352.8%
 
352.8%
 
m352.8%
 
N231.8%
 
S121.0%
 
O40.3%
 
n40.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter99579.9%
 
Uppercase Letter21517.3%
 
Space Separator352.8%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A13160.9%
 
E4520.9%
 
N2310.7%
 
S125.6%
 
O41.9%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
r15715.8%
 
a13914.0%
 
i13513.6%
 
c939.3%
 
e848.4%
 
o808.0%
 
u575.7%
 
f545.4%
 
p454.5%
 
s424.2%
 
t353.5%
 
h353.5%
 
m353.5%
 
n40.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
35100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin121097.2%
 
Common352.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
r15713.0%
 
a13911.5%
 
i13511.2%
 
A13110.8%
 
c937.7%
 
e846.9%
 
o806.6%
 
u574.7%
 
f544.5%
 
E453.7%
 
p453.7%
 
s423.5%
 
t352.9%
 
h352.9%
 
m352.9%
 
N231.9%
 
S121.0%
 
O40.3%
 
n40.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
35100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1245100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
r15712.6%
 
a13911.2%
 
i13510.8%
 
A13110.5%
 
c937.5%
 
e846.7%
 
o806.4%
 
u574.6%
 
f544.3%
 
E453.6%
 
p453.6%
 
s423.4%
 
t352.8%
 
h352.8%
 
352.8%
 
m352.8%
 
N231.8%
 
S121.0%
 
O40.3%
 
n40.3%
 

Country
Categorical

UNIQUE

Distinct180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
United Kingdom
 
1
Seychelles
 
1
Turkey
 
1
Russia
 
1
Ireland
 
1
Other values (175)
175 
ValueCountFrequency (%) 
United Kingdom10.6%
 
Seychelles10.6%
 
Turkey10.6%
 
Russia10.6%
 
Ireland10.6%
 
Mexico10.6%
 
Hungary10.6%
 
Luxembourg10.6%
 
Burkina Faso10.6%
 
Brazil10.6%
 
Bhutan10.6%
 
Israel10.6%
 
Sri Lanka10.6%
 
Palestine10.6%
 
Papua New Guinea10.6%
 
Pakistan10.6%
 
Mauritius10.6%
 
Fiji10.6%
 
Greece10.6%
 
Belgium10.6%
 
South Africa10.6%
 
Rwanda10.6%
 
Estonia10.6%
 
Tanzania10.6%
 
Croatia10.6%
 
Other values (155)15586.1%
 
2020-10-15T23:42:29.640895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique180 ?
Unique (%)100.0%
2020-10-15T23:42:29.874840image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length7
Mean length8.422222222
Min length3

Overview of Unicode Properties

Unique unicode characters55
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a22915.1%
 
i1348.8%
 
n1197.8%
 
e1117.3%
 
r825.4%
 
o765.0%
 
u583.8%
 
t563.7%
 
l523.4%
 
d483.2%
 
463.0%
 
s412.7%
 
g312.0%
 
m312.0%
 
b291.9%
 
S281.8%
 
c271.8%
 
h231.5%
 
B191.3%
 
p191.3%
 
C181.2%
 
y181.2%
 
M171.1%
 
A151.0%
 
G140.9%
 
Other values (30)17511.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter124081.8%
 
Uppercase Letter22014.5%
 
Space Separator463.0%
 
Other Punctuation90.6%
 
Dash Punctuation10.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S2812.7%
 
B198.6%
 
C188.2%
 
M177.7%
 
A156.8%
 
G146.4%
 
L125.5%
 
R115.0%
 
N104.5%
 
P104.5%
 
I94.1%
 
T94.1%
 
E83.6%
 
K73.2%
 
U73.2%
 
D52.3%
 
H41.8%
 
F41.8%
 
V41.8%
 
J31.4%
 
Z31.4%
 
O10.5%
 
Q10.5%
 
Y10.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a22918.5%
 
i13410.8%
 
n1199.6%
 
e1119.0%
 
r826.6%
 
o766.1%
 
u584.7%
 
t564.5%
 
l524.2%
 
d483.9%
 
s413.3%
 
g312.5%
 
m312.5%
 
b292.3%
 
c272.2%
 
h231.9%
 
p191.5%
 
y181.5%
 
z131.0%
 
w110.9%
 
v100.8%
 
k100.8%
 
j40.3%
 
f30.2%
 
q30.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
46100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.666.7%
 
,222.2%
 
'111.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin146096.3%
 
Common563.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a22915.7%
 
i1349.2%
 
n1198.2%
 
e1117.6%
 
r825.6%
 
o765.2%
 
u584.0%
 
t563.8%
 
l523.6%
 
d483.3%
 
s412.8%
 
g312.1%
 
m312.1%
 
b292.0%
 
S281.9%
 
c271.8%
 
h231.6%
 
B191.3%
 
p191.3%
 
C181.2%
 
y181.2%
 
M171.2%
 
A151.0%
 
G141.0%
 
z130.9%
 
Other values (25)15210.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
4682.1%
 
.610.7%
 
,23.6%
 
'11.8%
 
-11.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1516100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a22915.1%
 
i1348.8%
 
n1197.8%
 
e1117.3%
 
r825.4%
 
o765.0%
 
u583.8%
 
t563.7%
 
l523.4%
 
d483.2%
 
463.0%
 
s412.7%
 
g312.0%
 
m312.0%
 
b291.9%
 
S281.8%
 
c271.8%
 
h231.5%
 
B191.3%
 
p191.3%
 
C181.2%
 
y181.2%
 
M171.1%
 
A151.0%
 
G140.9%
 
Other values (30)17511.5%
 

Population_Density
Real number (ℝ≥0)

Distinct165
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean359.0886667
Minimum2.11
Maximum26300
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-15T23:42:30.109727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2.11
5-th percentile4.492
Q134.825
median85.3
Q3208
95-th percentile628.05
Maximum26300
Range26297.89
Interquartile range (IQR)173.175

Descriptive statistics

Standard deviation2056.723956
Coefficient of variation (CV)5.727621468
Kurtosis144.4765355
Mean359.0886667
Median Absolute Deviation (MAD)59.95
Skewness11.67292666
Sum64635.96
Variance4230113.432
MonotocityNot monotonic
2020-10-15T23:42:30.368554image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
13731.7%
 
16.221.1%
 
20321.1%
 
4.1521.1%
 
24021.1%
 
8321.1%
 
11121.1%
 
18.321.1%
 
10321.1%
 
12321.1%
 
27321.1%
 
16.521.1%
 
19.821.1%
 
11521.1%
 
23810.6%
 
98.510.6%
 
24210.6%
 
38.410.6%
 
52.510.6%
 
180010.6%
 
3410.6%
 
94.510.6%
 
34710.6%
 
20610.6%
 
51.610.6%
 
Other values (140)14077.8%
 
ValueCountFrequency (%) 
2.1110.6%
 
3.0910.6%
 
3.3210.6%
 
3.410.6%
 
3.7610.6%
 
3.9110.6%
 
410.6%
 
4.1521.1%
 
4.5110.6%
 
6.9610.6%
 
ValueCountFrequency (%) 
2630010.6%
 
836010.6%
 
224010.6%
 
180010.6%
 
138010.6%
 
127010.6%
 
84710.6%
 
66810.6%
 
66710.6%
 
62610.6%
 

Fertility_Rate
Real number (ℝ≥0)

MISSING

Distinct134
Distinct (%)77.0%
Missing6
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean2.649137931
Minimum1.23
Maximum7
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-15T23:42:30.622024image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.23
5-th percentile1.42
Q11.7325
median2.155
Q33.47
95-th percentile5.175
Maximum7
Range5.77
Interquartile range (IQR)1.7375

Descriptive statistics

Standard deviation1.238361239
Coefficient of variation (CV)0.4674581964
Kurtosis0.3732175691
Mean2.649137931
Median Absolute Deviation (MAD)0.55
Skewness1.137918631
Sum460.95
Variance1.533538559
MonotocityNot monotonic
2020-10-15T23:42:30.850250image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.9452.8%
 
1.4242.2%
 
2.3842.2%
 
231.7%
 
1.6931.7%
 
1.9531.7%
 
1.5631.7%
 
1.9731.7%
 
2.7331.7%
 
1.621.1%
 
4.3421.1%
 
2.4121.1%
 
2.221.1%
 
1.7321.1%
 
1.721.1%
 
2.1321.1%
 
2.2321.1%
 
1.4921.1%
 
1.5921.1%
 
1.8121.1%
 
1.7721.1%
 
1.8321.1%
 
2.0121.1%
 
2.3521.1%
 
3.4721.1%
 
Other values (109)11161.7%
 
(Missing)63.3%
 
ValueCountFrequency (%) 
1.2310.6%
 
1.2410.6%
 
1.2710.6%
 
1.2910.6%
 
1.310.6%
 
1.3310.6%
 
1.3610.6%
 
1.4242.2%
 
1.4321.1%
 
1.4410.6%
 
ValueCountFrequency (%) 
710.6%
 
5.8910.6%
 
5.7210.6%
 
5.710.6%
 
5.5510.6%
 
5.4110.6%
 
5.3810.6%
 
5.2510.6%
 
5.2410.6%
 
5.1410.6%
 

GDP_PPP
Real number (ℝ≥0)

Distinct167
Distinct (%)93.3%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean19855.7933
Minimum628
Maximum116000
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-15T23:42:31.054479image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum628
5-th percentile1492
Q14465
median12800
Q329750
95-th percentile59390
Maximum116000
Range115372
Interquartile range (IQR)25285

Descriptive statistics

Standard deviation20413.57936
Coefficient of variation (CV)1.028091855
Kurtosis3.570569126
Mean19855.7933
Median Absolute Deviation (MAD)9890
Skewness1.715970062
Sum3554187
Variance416714222.2
MonotocityNot monotonic
2020-10-15T23:42:31.283964image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1020031.7%
 
1710021.1%
 
1320021.1%
 
3290021.1%
 
1810021.1%
 
1820021.1%
 
1600021.1%
 
121021.1%
 
4040021.1%
 
1130021.1%
 
1400021.1%
 
1770010.6%
 
820010.6%
 
729010.6%
 
4890010.6%
 
3440010.6%
 
1590010.6%
 
2270010.6%
 
231010.6%
 
2430010.6%
 
3620010.6%
 
1450010.6%
 
3000010.6%
 
4730010.6%
 
3670010.6%
 
Other values (142)14278.9%
 
ValueCountFrequency (%) 
62810.6%
 
63310.6%
 
81710.6%
 
84510.6%
 
97410.6%
 
113010.6%
 
121021.1%
 
142010.6%
 
150010.6%
 
152010.6%
 
ValueCountFrequency (%) 
11600010.6%
 
9510010.6%
 
9050010.6%
 
7510010.6%
 
7410010.6%
 
6750010.6%
 
6530010.6%
 
6430010.6%
 
6200010.6%
 
5910010.6%
 

Corruption
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct177
Distinct (%)100.0%
Missing3
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean-0.06940283898
Minimum-1.800085
Maximum2.21243
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-15T23:42:31.517492image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.800085
5-th percentile-1.4733392
Q1-0.8228126
median-0.2712442
Q30.5782577
95-th percentile2.0079732
Maximum2.21243
Range4.012515
Interquartile range (IQR)1.4010703

Descriptive statistics

Standard deviation1.012634121
Coefficient of variation (CV)-14.59067289
Kurtosis-0.4163954688
Mean-0.06940283898
Median Absolute Deviation (MAD)0.6309404
Skewness0.6069606266
Sum-12.2843025
Variance1.025427864
MonotocityNot monotonic
2020-10-15T23:42:33.216897image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.216403410.6%
 
0.545733710.6%
 
0.149542110.6%
 
0.768825510.6%
 
0.536202810.6%
 
-0.211484410.6%
 
-0.485356710.6%
 
0.246082810.6%
 
1.84364910.6%
 
2.00902610.6%
 
-0.521866710.6%
 
-0.02156910.6%
 
-0.954297210.6%
 
0.849830410.6%
 
-0.544815410.6%
 
1.54737610.6%
 
2.17469610.6%
 
-0.540105210.6%
 
1.42477210.6%
 
-0.873690810.6%
 
0.276634610.6%
 
0.183871210.6%
 
-0.31726710.6%
 
-1.46751210.6%
 
-0.63646510.6%
 
Other values (152)15284.4%
 
(Missing)31.7%
 
ValueCountFrequency (%) 
-1.80008510.6%
 
-1.72795410.6%
 
-1.63797110.6%
 
-1.62681210.6%
 
-1.55968410.6%
 
-1.55271110.6%
 
-1.53372410.6%
 
-1.50339810.6%
 
-1.49664810.6%
 
-1.46751210.6%
 
ValueCountFrequency (%) 
2.2124310.6%
 
2.17469610.6%
 
2.17452410.6%
 
2.14779510.6%
 
2.13957810.6%
 
2.08990710.6%
 
2.08775510.6%
 
2.0097110.6%
 
2.00902610.6%
 
2.0077110.6%
 

Government_Effectiveness
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct176
Distinct (%)99.4%
Missing3
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean-0.04511925085
Minimum-2.449409
Maximum2.231474
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-15T23:42:33.429733image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.449409
5-th percentile-1.6237124
Q1-0.6773402
median-0.153388
Q30.5391499
95-th percentile1.7468738
Maximum2.231474
Range4.680883
Interquartile range (IQR)1.2164901

Descriptive statistics

Standard deviation1.005240362
Coefficient of variation (CV)-22.27963326
Kurtosis-0.4528773186
Mean-0.04511925085
Median Absolute Deviation (MAD)0.6075022
Skewness0.200465328
Sum-7.9861074
Variance1.010508185
MonotocityNot monotonic
2020-10-15T23:42:33.623333image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.195670321.1%
 
-1.84703710.6%
 
-0.760890210.6%
 
-0.682006110.6%
 
-0.56860510.6%
 
-1.52884710.6%
 
0.50228410.6%
 
-0.606039910.6%
 
-0.970923210.6%
 
-0.640202310.6%
 
0.111619810.6%
 
-0.209295910.6%
 
-0.570518610.6%
 
-1.4878510.6%
 
1.87163110.6%
 
1.94497610.6%
 
0.349959610.6%
 
-1.45728510.6%
 
1.20890910.6%
 
1.71610810.6%
 
-1.1988610.6%
 
-1.55478710.6%
 
0.021720410.6%
 
-1.58151710.6%
 
0.333441110.6%
 
Other values (151)15183.9%
 
(Missing)31.7%
 
ValueCountFrequency (%) 
-2.44940910.6%
 
-2.24435410.6%
 
-2.1910310.6%
 
-1.90905410.6%
 
-1.84703710.6%
 
-1.72773210.6%
 
-1.72041710.6%
 
-1.67116910.6%
 
-1.63913410.6%
 
-1.61985710.6%
 
ValueCountFrequency (%) 
2.23147410.6%
 
2.03962110.6%
 
1.98420810.6%
 
1.94497610.6%
 
1.88711910.6%
 
1.87163110.6%
 
1.85046610.6%
 
1.83097510.6%
 
1.77822110.6%
 
1.73903710.6%
 

Political_Stability
Real number (ℝ)

Distinct179
Distinct (%)100.0%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean-0.1350782933
Minimum-3.002496
Maximum1.612102
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-15T23:42:33.821997image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-3.002496
5-th percentile-2.1954071
Q1-0.69407435
median-0.0298916
Q30.68769605
95-th percentile1.2104534
Maximum1.612102
Range4.614598
Interquartile range (IQR)1.3817704

Descriptive statistics

Standard deviation0.9986789275
Coefficient of variation (CV)-7.393333918
Kurtosis0.03186854416
Mean-0.1350782933
Median Absolute Deviation (MAD)0.6844828
Skewness-0.6286294094
Sum-24.1790145
Variance0.9973596002
MonotocityNot monotonic
2020-10-15T23:42:34.018123image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.666998110.6%
 
-0.119297510.6%
 
0.854048910.6%
 
-0.074749810.6%
 
0.910918310.6%
 
-0.259300510.6%
 
0.979156210.6%
 
-2.11620510.6%
 
0.788799510.6%
 
-0.813388510.6%
 
1.48419110.6%
 
-0.331886210.6%
 
0.311193310.6%
 
0.109134810.6%
 
0.277154710.6%
 
-0.559101610.6%
 
0.382110.6%
 
0.750438310.6%
 
0.237390110.6%
 
-1.16493110.6%
 
0.486915410.6%
 
1.14003110.6%
 
-0.840442410.6%
 
0.917523210.6%
 
-1.12184410.6%
 
Other values (154)15485.6%
 
ValueCountFrequency (%) 
-3.00249610.6%
 
-2.7467310.6%
 
-2.73962310.6%
 
-2.5558610.6%
 
-2.44138810.6%
 
-2.43931610.6%
 
-2.28126110.6%
 
-2.26743510.6%
 
-2.21977110.6%
 
-2.192710.6%
 
ValueCountFrequency (%) 
1.61210210.6%
 
1.54148210.6%
 
1.51033910.6%
 
1.48419110.6%
 
1.42543210.6%
 
1.41242610.6%
 
1.37340410.6%
 
1.34486310.6%
 
1.29419310.6%
 
1.20114910.6%
 

Rule_of_Law
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct177
Distinct (%)100.0%
Missing3
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean-0.08269168362
Minimum-2.338622
Maximum2.046279
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-15T23:42:34.215789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.338622
5-th percentile-1.6723208
Q1-0.7695495
median-0.2400078
Q30.5333768
95-th percentile1.811981
Maximum2.046279
Range4.384901
Interquartile range (IQR)1.3029263

Descriptive statistics

Standard deviation0.9973164957
Coefficient of variation (CV)-12.0606626
Kurtosis-0.4325031485
Mean-0.08269168362
Median Absolute Deviation (MAD)0.6409153
Skewness0.3028279321
Sum-14.636428
Variance0.9946401927
MonotocityNot monotonic
2020-10-15T23:42:34.400127image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.46422410.6%
 
0.412708810.6%
 
1.14091210.6%
 
-0.407808910.6%
 
-0.483719610.6%
 
-1.23404310.6%
 
0.559249610.6%
 
-0.549597310.6%
 
0.957360410.6%
 
-1.5864310.6%
 
-2.04786810.6%
 
0.026323410.6%
 
-0.003282310.6%
 
-0.686367610.6%
 
-0.128658510.6%
 
-0.444349610.6%
 
0.599107410.6%
 
-0.477669410.6%
 
0.993909610.6%
 
-0.29470510.6%
 
0.734673710.6%
 
1.05850510.6%
 
-0.579592510.6%
 
0.32478610.6%
 
1.81636110.6%
 
Other values (152)15284.4%
 
(Missing)31.7%
 
ValueCountFrequency (%) 
-2.33862210.6%
 
-2.33278610.6%
 
-2.04786810.6%
 
-1.95840810.6%
 
-1.79039410.6%
 
-1.7858310.6%
 
-1.78422110.6%
 
-1.75929110.6%
 
-1.68769210.6%
 
-1.66847810.6%
 
ValueCountFrequency (%) 
2.04627910.6%
 
1.96563110.6%
 
1.93122210.6%
 
1.89598310.6%
 
1.87629410.6%
 
1.87583410.6%
 
1.84489110.6%
 
1.83337810.6%
 
1.81636110.6%
 
1.81088610.6%
 

Government_Healthcare_Spend
Real number (ℝ≥0)

MISSING

Distinct170
Distinct (%)100.0%
Missing10
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean0.5112119475
Minimum0.05095819
Maximum0.9482338
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-15T23:42:34.594988image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.05095819
5-th percentile0.135063865
Q10.34986995
median0.5137988
Q30.6971453
95-th percentile0.83304401
Maximum0.9482338
Range0.89727561
Interquartile range (IQR)0.34727535

Descriptive statistics

Standard deviation0.2201398425
Coefficient of variation (CV)0.4306234304
Kurtosis-0.9209722297
Mean0.5112119475
Median Absolute Deviation (MAD)0.17560175
Skewness-0.2328012027
Sum86.90603107
Variance0.04846155025
MonotocityNot monotonic
2020-10-15T23:42:34.776669image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.746641210.6%
 
0.662308910.6%
 
0.894421310.6%
 
0.770919510.6%
 
0.209786310.6%
 
0.15938710.6%
 
0.518584710.6%
 
0.127480310.6%
 
0.840240310.6%
 
0.166933410.6%
 
0.388010210.6%
 
0.566733510.6%
 
0.442008910.6%
 
0.137371510.6%
 
0.407357610.6%
 
0.46601810.6%
 
0.718181810.6%
 
0.848564910.6%
 
0.299844710.6%
 
0.489512310.6%
 
0.33470410.6%
 
0.772173910.6%
 
0.172045510.6%
 
0.418793810.6%
 
0.646982410.6%
 
Other values (145)14580.6%
 
(Missing)105.6%
 
ValueCountFrequency (%) 
0.0509581910.6%
 
0.0819773710.6%
 
0.0844199410.6%
 
0.0994900710.6%
 
0.118561910.6%
 
0.127480310.6%
 
0.128226310.6%
 
0.132446710.6%
 
0.133175810.6%
 
0.137371510.6%
 
ValueCountFrequency (%) 
0.948233810.6%
 
0.894421310.6%
 
0.877427710.6%
 
0.873870110.6%
 
0.854730510.6%
 
0.848564910.6%
 
0.840925210.6%
 
0.840240310.6%
 
0.836949210.6%
 
0.82827110.6%
 

Urban_Population
Real number (ℝ≥0)

MISSING

Distinct103
Distinct (%)87.3%
Missing62
Missing (%)34.4%
Infinite0
Infinite (%)0.0%
Mean0.2528135593
Minimum0.041
Maximum1
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-15T23:42:34.968219image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.041
5-th percentile0.069775
Q10.14325
median0.2145
Q30.317
95-th percentile0.5829
Maximum1
Range0.959
Interquartile range (IQR)0.17375

Descriptive statistics

Standard deviation0.1605374318
Coefficient of variation (CV)0.6350032498
Kurtosis3.697931997
Mean0.2528135593
Median Absolute Deviation (MAD)0.082
Skewness1.593177179
Sum29.832
Variance0.02577226699
MonotocityNot monotonic
2020-10-15T23:42:35.161697image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.16131.7%
 
0.12231.7%
 
0.32221.1%
 
0.22921.1%
 
0.46521.1%
 
0.42921.1%
 
0.18821.1%
 
0.2721.1%
 
0.24421.1%
 
0.14821.1%
 
0.17121.1%
 
0.30221.1%
 
0.64721.1%
 
0.14110.6%
 
0.2610.6%
 
0.11910.6%
 
0.28510.6%
 
0.09610.6%
 
0.098610.6%
 
0.36610.6%
 
0.33110.6%
 
0.29410.6%
 
0.60510.6%
 
0.13510.6%
 
0.15510.6%
 
Other values (78)7843.3%
 
(Missing)6234.4%
 
ValueCountFrequency (%) 
0.04110.6%
 
0.046810.6%
 
0.048110.6%
 
0.053710.6%
 
0.057710.6%
 
0.063410.6%
 
0.070910.6%
 
0.074210.6%
 
0.08610.6%
 
0.086710.6%
 
ValueCountFrequency (%) 
110.6%
 
0.72610.6%
 
0.64721.1%
 
0.60910.6%
 
0.60510.6%
 
0.57910.6%
 
0.50410.6%
 
0.50210.6%
 
0.48110.6%
 
0.47510.6%
 

Smoking_Prevalence
Real number (ℝ≥0)

MISSING

Distinct115
Distinct (%)83.3%
Missing42
Missing (%)23.3%
Infinite0
Infinite (%)0.0%
Mean0.2123188406
Minimum0.02
Maximum0.459
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-15T23:42:35.352891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.06355
Q10.13925
median0.217
Q30.27125
95-th percentile0.3712
Maximum0.459
Range0.439
Interquartile range (IQR)0.132

Descriptive statistics

Standard deviation0.09354705172
Coefficient of variation (CV)0.4405970354
Kurtosis-0.4649750124
Mean0.2123188406
Median Absolute Deviation (MAD)0.0705
Skewness0.203081522
Sum29.3
Variance0.008751050885
MonotocityNot monotonic
2020-10-15T23:42:35.543022image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.3731.7%
 
0.28931.7%
 
0.12321.1%
 
0.11521.1%
 
0.1421.1%
 
0.25221.1%
 
0.22521.1%
 
0.25621.1%
 
0.14721.1%
 
0.15621.1%
 
0.22821.1%
 
0.28821.1%
 
0.38921.1%
 
0.20321.1%
 
0.32721.1%
 
0.16821.1%
 
0.21821.1%
 
0.21521.1%
 
0.24321.1%
 
0.26721.1%
 
0.30621.1%
 
0.12710.6%
 
0.29610.6%
 
0.1310.6%
 
0.36410.6%
 
Other values (90)9050.0%
 
(Missing)4223.3%
 
ValueCountFrequency (%) 
0.0210.6%
 
0.03910.6%
 
0.04410.6%
 
0.04810.6%
 
0.05710.6%
 
0.05810.6%
 
0.06110.6%
 
0.06410.6%
 
0.07110.6%
 
0.07410.6%
 
ValueCountFrequency (%) 
0.45910.6%
 
0.43410.6%
 
0.39410.6%
 
0.39310.6%
 
0.38921.1%
 
0.37810.6%
 
0.3731.7%
 
0.36410.6%
 
0.36310.6%
 
0.35210.6%
 

Tourism
Real number (ℝ≥0)

MISSING

Distinct145
Distinct (%)99.3%
Missing34
Missing (%)18.9%
Infinite0
Infinite (%)0.0%
Mean9147278.904
Minimum14000
Maximum89322000
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-15T23:42:35.756634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum14000
5-th percentile95225
Q1854250
median2784000
Q310576250
95-th percentile38705250
Maximum89322000
Range89308000
Interquartile range (IQR)9722000

Descriptive statistics

Standard deviation15687127.84
Coefficient of variation (CV)1.714950206
Kurtosis10.89228908
Mean9147278.904
Median Absolute Deviation (MAD)2512500
Skewness3.120811174
Sum1335502720
Variance2.460859798e+14
MonotocityNot monotonic
2020-10-15T23:42:35.940532image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
117300021.1%
 
2583200010.6%
 
1172000010.6%
 
1410400010.6%
 
534600010.6%
 
139900010.6%
 
87100010.6%
 
178500010.6%
 
924600010.6%
 
911900010.6%
 
1119600010.6%
 
48900010.6%
 
34700010.6%
 
233400010.6%
 
263300010.6%
 
68000010.6%
 
57300010.6%
 
165200010.6%
 
37500010.6%
 
1467300010.6%
 
114200010.6%
 
322400010.6%
 
225600010.6%
 
301700010.6%
 
1092600010.6%
 
Other values (120)12066.7%
 
(Missing)3418.9%
 
ValueCountFrequency (%) 
1400010.6%
 
3340010.6%
 
3590010.6%
 
5700010.6%
 
6300010.6%
 
8000010.6%
 
8400010.6%
 
8530010.6%
 
12500010.6%
 
14000010.6%
 
ValueCountFrequency (%) 
8932200010.6%
 
8277300010.6%
 
7974592010.6%
 
6290000010.6%
 
6156720010.6%
 
4576800010.6%
 
4131300010.6%
 
3888100010.6%
 
3817800010.6%
 
3631600010.6%
 

Women_In_Parliament
Real number (ℝ≥0)

MISSING

Distinct164
Distinct (%)92.7%
Missing3
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean0.2361692774
Minimum0
Maximum0.6125
Zeros1
Zeros (%)0.6%
Memory size1.5 KiB
2020-10-15T23:42:36.144311image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05772549
Q10.15
median0.2142857
Q30.3088858
95-th percentile0.46363802
Maximum0.6125
Range0.6125
Interquartile range (IQR)0.1588858

Descriptive statistics

Standard deviation0.1197742097
Coefficient of variation (CV)0.5071540676
Kurtosis-0.0531674551
Mean0.2361692774
Median Absolute Deviation (MAD)0.0726708
Skewness0.5542761457
Sum41.8019621
Variance0.01434586131
MonotocityNot monotonic
2020-10-15T23:42:36.336270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.242.2%
 
0.2531.7%
 
0.331.7%
 
0.1521.1%
 
0.233333321.1%
 
0.225806421.1%
 
0.123287721.1%
 
0.166666721.1%
 
0.309523821.1%
 
0.243636410.6%
 
0.157777810.6%
 
0.095238110.6%
 
0.00332225910.6%
 
0.0254237310.6%
 
0.17391310.6%
 
0.2210.6%
 
0.205188710.6%
 
0.229166710.6%
 
0.408333310.6%
 
0.348583910.6%
 
0.4210.6%
 
0.363636410.6%
 
0.368956710.6%
 
0.258333310.6%
 
0.327272710.6%
 
Other values (139)13977.2%
 
(Missing)31.7%
 
ValueCountFrequency (%) 
010.6%
 
0.00332225910.6%
 
0.0232558110.6%
 
0.0254237310.6%
 
0.0338028210.6%
 
0.0459770110.6%
 
0.0461538510.6%
 
0.04687510.6%
 
0.0533333310.6%
 
0.0588235310.6%
 
ValueCountFrequency (%) 
0.612510.6%
 
0.532231410.6%
 
0.530769210.6%
 
0.510.6%
 
0.48210.6%
 
0.472779410.6%
 
0.4710.6%
 
0.466666710.6%
 
0.464285710.6%
 
0.463476110.6%
 

Obesity_Rate
Real number (ℝ≥0)

MISSING

Distinct130
Distinct (%)73.9%
Missing4
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean0.1820340908
Minimum0.021
Maximum0.379
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-15T23:42:36.532928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.021
5-th percentile0.0515
Q10.08775
median0.202
Q30.24625
95-th percentile0.31775
Maximum0.379
Range0.358
Interquartile range (IQR)0.1585

Descriptive statistics

Standard deviation0.08893117079
Coefficient of variation (CV)0.4885412969
Kurtosis-1.081833978
Mean0.1820340908
Median Absolute Deviation (MAD)0.0675
Skewness-0.08745829229
Sum32.03799998
Variance0.007908753139
MonotocityNot monotonic
2020-10-15T23:42:36.733859image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.23142.2%
 
0.20242.2%
 
0.18931.7%
 
0.05831.7%
 
0.19931.7%
 
0.16631.7%
 
0.19731.7%
 
0.05331.7%
 
0.08631.7%
 
0.10321.1%
 
0.27821.1%
 
0.22721.1%
 
0.06621.1%
 
0.28321.1%
 
0.20621.1%
 
0.06121.1%
 
0.22321.1%
 
0.24121.1%
 
0.06421.1%
 
0.22121.1%
 
0.09621.1%
 
0.24621.1%
 
0.26121.1%
 
0.05521.1%
 
0.28921.1%
 
Other values (105)11563.9%
 
(Missing)42.2%
 
ValueCountFrequency (%) 
0.02110.6%
 
0.03610.6%
 
0.03921.1%
 
0.04110.6%
 
0.04310.6%
 
0.04510.6%
 
0.04710.6%
 
0.0510.6%
 
0.05210.6%
 
0.05331.7%
 
ValueCountFrequency (%) 
0.37910.6%
 
0.36210.6%
 
0.35510.6%
 
0.35410.6%
 
0.35110.6%
 
0.32510.6%
 
0.32110.6%
 
0.3221.1%
 
0.31710.6%
 
0.31610.6%
 

Year
Categorical

MISSING

Distinct1
Distinct (%)0.6%
Missing7
Missing (%)3.9%
Memory size1.5 KiB
2019
173 
ValueCountFrequency (%) 
201917396.1%
 
(Missing)73.9%
 
2020-10-15T23:42:36.919061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-15T23:42:37.007223image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:37.108008image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length5.883333333
Min length3

Overview of Unicode Properties

Unique unicode characters7
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
034632.7%
 
217316.3%
 
117316.3%
 
917316.3%
 
.17316.3%
 
n141.3%
 
a70.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number86581.7%
 
Other Punctuation17316.3%
 
Lowercase Letter212.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
034640.0%
 
217320.0%
 
117320.0%
 
917320.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.173100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n1466.7%
 
a733.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common103898.0%
 
Latin212.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
034633.3%
 
217316.7%
 
117316.7%
 
917316.7%
 
.17316.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n1466.7%
 
a733.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1059100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
034632.7%
 
217316.3%
 
117316.3%
 
917316.3%
 
.17316.3%
 
n141.3%
 
a70.7%
 

Diabetes Prevelance % of Population 20-79
Real number (ℝ≥0)

MISSING

Distinct83
Distinct (%)48.0%
Missing7
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean7.616763006
Minimum1
Maximum22.1
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-15T23:42:37.274703image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.4
Q15.1
median6.6
Q39.6
95-th percentile15.6
Maximum22.1
Range21.1
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.900507858
Coefficient of variation (CV)0.512095211
Kurtosis1.705526714
Mean7.616763006
Median Absolute Deviation (MAD)2.1
Skewness1.144541437
Sum1317.7
Variance15.21396155
MonotocityNot monotonic
2020-10-15T23:42:37.458904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2.4116.1%
 
695.0%
 
6.184.4%
 
4.573.9%
 
952.8%
 
5.152.8%
 
542.2%
 
742.2%
 
9.242.2%
 
6.942.2%
 
11.642.2%
 
5.831.7%
 
5.431.7%
 
5.631.7%
 
5.731.7%
 
7.331.7%
 
8.831.7%
 
10.431.7%
 
9.631.7%
 
13.521.1%
 
5.921.1%
 
8.621.1%
 
6.621.1%
 
4.821.1%
 
12.321.1%
 
Other values (58)7240.0%
 
(Missing)73.9%
 
ValueCountFrequency (%) 
110.6%
 
1.810.6%
 
1.910.6%
 
2.4116.1%
 
2.521.1%
 
3.121.1%
 
3.210.6%
 
3.310.6%
 
3.810.6%
 
3.921.1%
 
ValueCountFrequency (%) 
22.110.6%
 
2210.6%
 
19.910.6%
 
17.210.6%
 
17.110.6%
 
16.710.6%
 
16.310.6%
 
15.810.6%
 
15.621.1%
 
14.710.6%
 

Labour Force Total
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct167
Distinct (%)100.0%
Missing13
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean20422471.75
Minimum56676
Maximum781074570
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-15T23:42:37.660343image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum56676
5-th percentile224401.2
Q11625735
median4978166
Q313304032
95-th percentile65659431.4
Maximum781074570
Range781017894
Interquartile range (IQR)11678297

Descriptive statistics

Standard deviation73574776.57
Coefficient of variation (CV)3.602638186
Kurtosis79.19732275
Mean20422471.75
Median Absolute Deviation (MAD)4074983
Skewness8.447991625
Sum3410552782
Variance5.413247747e+15
MonotocityNot monotonic
2020-10-15T23:42:37.863631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
673498010.6%
 
599555610.6%
 
807923010.6%
 
477660210.6%
 
854553710.6%
 
1438760410.6%
 
79965210.6%
 
370015210.6%
 
684091710.6%
 
17851410.6%
 
472062910.6%
 
2312009710.6%
 
145729210.6%
 
281801110.6%
 
61068510.6%
 
1688587510.6%
 
5736465010.6%
 
1069979710.6%
 
2594612810.6%
 
3336884310.6%
 
218455110.6%
 
352706510.6%
 
2469058810.6%
 
148863210.6%
 
380874010.6%
 
Other values (142)14278.9%
 
(Missing)137.2%
 
ValueCountFrequency (%) 
5667610.6%
 
7189910.6%
 
10032210.6%
 
15529610.6%
 
17851410.6%
 
21686010.6%
 
21728410.6%
 
21784010.6%
 
22359310.6%
 
22628710.6%
 
ValueCountFrequency (%) 
78107457010.6%
 
49426142610.6%
 
16589006910.6%
 
13477652510.6%
 
10650083810.6%
 
7394376610.6%
 
7302568410.6%
 
7000946110.6%
 
6813908810.6%
 
5987356610.6%
 

Population
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct172
Distinct (%)100.0%
Missing8
Missing (%)4.4%
Infinite0
Infinite (%)0.0%
Mean44012474.48
Minimum71808
Maximum1397715000
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-15T23:42:38.068944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum71808
5-th percentile327883.4
Q12924757
median10185555.5
Q332089946
95-th percentile135134631.7
Maximum1397715000
Range1397643192
Interquartile range (IQR)29165189

Descriptive statistics

Standard deviation153483082.6
Coefficient of variation (CV)3.4872632
Kurtosis67.10892129
Mean44012474.48
Median Absolute Deviation (MAD)8889405
Skewness7.920356201
Sum7570145611
Variance2.355705665e+16
MonotocityNot monotonic
2020-10-15T23:42:38.256790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
132659010.6%
 
8291390610.6%
 
1028545310.6%
 
857483210.6%
 
21104952710.6%
 
265763710.6%
 
10811661510.6%
 
932101810.6%
 
4707678110.6%
 
1169471910.6%
 
139497310.6%
 
88995310.6%
 
1126307710.6%
 
677745210.6%
 
12626493110.6%
 
285419110.6%
 
1071632210.6%
 
21505610.6%
 
3647176910.6%
 
1594687610.6%
 
11207873010.6%
 
28702510.6%
 
5800546310.6%
 
8313279910.6%
 
1895203810.6%
 
Other values (147)14781.7%
 
(Missing)84.4%
 
ValueCountFrequency (%) 
7180810.6%
 
7714210.6%
 
9711810.6%
 
9762510.6%
 
11058910.6%
 
11200310.6%
 
18279010.6%
 
21505610.6%
 
28702510.6%
 
36131310.6%
 
ValueCountFrequency (%) 
139771500010.6%
 
136641775410.6%
 
32823952310.6%
 
27062556810.6%
 
21656531810.6%
 
21104952710.6%
 
20096359910.6%
 
16304616110.6%
 
14437353510.6%
 
12757552910.6%
 

Population Aged 65 and above % of Total
Real number (ℝ≥0)

MISSING

Distinct170
Distinct (%)100.0%
Missing10
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean9.095506672
Minimum1.156549456
Maximum28.00204928
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-15T23:42:38.454081image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.156549456
5-th percentile2.435345257
Q13.313903764
median6.816177807
Q315.03044278
95-th percentile20.62556164
Maximum28.00204928
Range26.84549982
Interquartile range (IQR)11.71653902

Descriptive statistics

Standard deviation6.514732166
Coefficient of variation (CV)0.7162583021
Kurtosis-0.7111993601
Mean9.095506672
Median Absolute Deviation (MAD)3.933280927
Skewness0.7586270769
Sum1546.236134
Variance42.44173519
MonotocityNot monotonic
2020-10-15T23:42:38.659988image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2.90214067510.6%
 
2.51880972910.6%
 
10.0350128610.6%
 
15.1921210710.6%
 
5.41525560810.6%
 
9.88043532310.6%
 
5.78256761710.6%
 
2.59671142610.6%
 
8.72628382610.6%
 
2.88734759110.6%
 
2.94773662310.6%
 
11.4721930210.6%
 
21.9414701310.6%
 
2.72364990810.6%
 
6.4463891210.6%
 
10.8388057610.6%
 
17.2011511710.6%
 
16.2309903310.6%
 
16.7049903910.6%
 
2.86326045510.6%
 
9.67741935510.6%
 
22.3566907210.6%
 
3.04406412810.6%
 
20.1584127110.6%
 
7.27329163310.6%
 
Other values (145)14580.6%
 
(Missing)105.6%
 
ValueCountFrequency (%) 
1.15654945610.6%
 
1.52316311710.6%
 
1.96255018710.6%
 
2.11531473810.6%
 
2.1993417510.6%
 
2.31209531510.6%
 
2.40728753710.6%
 
2.4238267110.6%
 
2.42450880810.6%
 
2.44858980510.6%
 
ValueCountFrequency (%) 
28.0020492810.6%
 
23.0121366510.6%
 
22.3566907210.6%
 
22.1406446210.6%
 
21.9414701310.6%
 
21.5629923210.6%
 
21.2529672710.6%
 
20.8601352310.6%
 
20.8171727510.6%
 
20.3913702810.6%
 

Urban Population
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct172
Distinct (%)100.0%
Missing8
Missing (%)4.4%
Infinite0
Infinite (%)0.0%
Mean24497877.47
Minimum23800
Maximum842933962
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-15T23:42:38.866875image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum23800
5-th percentile169696.1
Q11839284.25
median5621299
Q316739502
95-th percentile90142355.65
Maximum842933962
Range842910162
Interquartile range (IQR)14900217.75

Descriptive statistics

Standard deviation78969669.29
Coefficient of variation (CV)3.223531075
Kurtosis73.76489147
Mean24497877.47
Median Absolute Deviation (MAD)4800484
Skewness7.928008851
Sum4213634924
Variance6.236208667e+15
MonotocityNot monotonic
2020-10-15T23:42:39.049154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
194969410.6%
 
632894810.6%
 
289008410.6%
 
921304810.6%
 
519441610.6%
 
469470210.6%
 
74194410.6%
 
161655010.6%
 
5097590310.6%
 
319930110.6%
 
371227310.6%
 
33771110.6%
 
33911010.6%
 
562643310.6%
 
2778336810.6%
 
91602410.6%
 
236264410.6%
 
174759310.6%
 
385023110.6%
 
20891210.6%
 
441821810.6%
 
5590831610.6%
 
420708310.6%
 
6098741710.6%
 
3428010.6%
 
Other values (147)14781.7%
 
(Missing)84.4%
 
ValueCountFrequency (%) 
2380010.6%
 
3428010.6%
 
4076510.6%
 
5083010.6%
 
5576210.6%
 
5818510.6%
 
6787310.6%
 
8943110.6%
 
15827710.6%
 
17903910.6%
 
ValueCountFrequency (%) 
84293396210.6%
 
47103152810.6%
 
27066302810.6%
 
18324164110.6%
 
15150972410.6%
 
11578241610.6%
 
10768388910.6%
 
10280694810.6%
 
10262685910.6%
 
7992776210.6%
 

Death Rate Per 1000 (2017)
Real number (ℝ≥0)

MISSING

Distinct161
Distinct (%)94.2%
Missing9
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean7.757730994
Minimum1.169
Maximum15.5
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-15T23:42:39.240513image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.169
5-th percentile4.304
Q15.854
median7.293
Q39.4795
95-th percentile12.959
Maximum15.5
Range14.331
Interquartile range (IQR)3.6255

Descriptive statistics

Standard deviation2.744546757
Coefficient of variation (CV)0.3537821509
Kurtosis0.3225349611
Mean7.757730994
Median Absolute Deviation (MAD)1.732
Skewness0.5450943033
Sum1326.572
Variance7.532536904
MonotocityNot monotonic
2020-10-15T23:42:39.422402image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9.131.7%
 
6.521.1%
 
8.04421.1%
 
10.721.1%
 
9.921.1%
 
9.221.1%
 
14.821.1%
 
521.1%
 
8.63821.1%
 
7.33910.6%
 
10.59210.6%
 
8.1810.6%
 
6.7210.6%
 
2.60110.6%
 
8.810.6%
 
5.84510.6%
 
7.1610.6%
 
7.2110.6%
 
6.07610.6%
 
6.55410.6%
 
5.39410.6%
 
7.55810.6%
 
4.68110.6%
 
2.36810.6%
 
5.01410.6%
 
Other values (136)13675.6%
 
(Missing)95.0%
 
ValueCountFrequency (%) 
1.16910.6%
 
1.42910.6%
 
2.36810.6%
 
2.46210.6%
 
2.60110.6%
 
2.88310.6%
 
3.44610.6%
 
3.84310.6%
 
4.26310.6%
 
4.34510.6%
 
ValueCountFrequency (%) 
15.510.6%
 
14.821.1%
 
14.65610.6%
 
14.510.6%
 
14.210.6%
 
13.510.6%
 
13.310.6%
 
1310.6%
 
12.91810.6%
 
12.67110.6%
 
Distinct173
Distinct (%)100.0%
Missing7
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean28.36060508
Minimum5.861331001
Maximum99.73437372
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2020-10-15T23:42:39.618716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum5.861331001
5-th percentile7.641269144
Q115.92604819
median22.40081712
Q337.9265032
95-th percentile67.94400637
Maximum99.73437372
Range93.87304272
Interquartile range (IQR)22.00045501

Descriptive statistics

Standard deviation19.25361149
Coefficient of variation (CV)0.6788857796
Kurtosis2.501458767
Mean28.36060508
Median Absolute Deviation (MAD)9.51429865
Skewness1.558937723
Sum4906.384679
Variance370.7015554
MonotocityNot monotonic
2020-10-15T23:42:39.826381image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
34.778700110.6%
 
25.1095383410.6%
 
8.55032440110.6%
 
22.2516710910.6%
 
32.3885049110.6%
 
19.9286636210.6%
 
28.5783742710.6%
 
40.695825910.6%
 
48.0301986810.6%
 
23.0118921110.6%
 
18.7655799110.6%
 
16.7507119410.6%
 
43.7572594710.6%
 
23.0943861810.6%
 
50.4560068910.6%
 
94.0538176810.6%
 
11.9090808510.6%
 
26.062490210.6%
 
11.8149636210.6%
 
16.2510327110.6%
 
7.79582079910.6%
 
17.9017722910.6%
 
16.0358087110.6%
 
19.4931537610.6%
 
35.5574529210.6%
 
Other values (148)14882.2%
 
(Missing)73.9%
 
ValueCountFrequency (%) 
5.86133100110.6%
 
5.90306546810.6%
 
5.95600112910.6%
 
6.1846650910.6%
 
6.42838313310.6%
 
6.48114726310.6%
 
6.73212401510.6%
 
6.95651980310.6%
 
7.40944166210.6%
 
7.79582079910.6%
 
ValueCountFrequency (%) 
99.7343737210.6%
 
94.0538176810.6%
 
91.1873279310.6%
 
90.8732104710.6%
 
87.9454466810.6%
 
86.9994520510.6%
 
72.7930962210.6%
 
71.7981743810.6%
 
70.8162076810.6%
 
66.029205510.6%
 
Distinct60
Distinct (%)34.7%
Missing7
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean88.92870066
Minimum0
Maximum100
Zeros6
Zeros (%)3.3%
Memory size1.5 KiB
2020-10-15T23:42:40.021567image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.519160577
Q198.31562394
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)1.68437606

Descriptive statistics

Standard deviation27.23932646
Coefficient of variation (CV)0.3063052339
Kurtosis5.083694805
Mean88.92870066
Median Absolute Deviation (MAD)0
Skewness-2.547905389
Sum15384.66521
Variance741.9809057
MonotocityNot monotonic
2020-10-15T23:42:40.228563image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10010960.6%
 
063.3%
 
91.60932910.6%
 
97.4325569210.6%
 
99.9982944610.6%
 
3.63648738210.6%
 
99.9629262910.6%
 
76.7615082310.6%
 
92.1046289810.6%
 
94.77721410.6%
 
99.0583082510.6%
 
99.4910252810.6%
 
99.9336624310.6%
 
66.5332490310.6%
 
0.27409167910.6%
 
89.1743564310.6%
 
56.9144561110.6%
 
99.9134426210.6%
 
90.5373438110.6%
 
71.6367476610.6%
 
99.9661157310.6%
 
16.0083566310.6%
 
2.043837810.6%
 
99.9026951310.6%
 
68.7756227410.6%
 
Other values (35)3519.4%
 
(Missing)73.9%
 
ValueCountFrequency (%) 
063.3%
 
0.27409167910.6%
 
2.043837810.6%
 
3.3431703710.6%
 
3.63648738210.6%
 
10.2852157910.6%
 
16.0083566310.6%
 
17.8244594910.6%
 
22.3857446610.6%
 
24.8935837710.6%
 
ValueCountFrequency (%) 
10010960.6%
 
99.9999999910.6%
 
99.9991320210.6%
 
99.9982944610.6%
 
99.9881773210.6%
 
99.9835217110.6%
 
99.9661157310.6%
 
99.9629262910.6%
 
99.9552350310.6%
 
99.9336624310.6%
 

Interactions

2020-10-15T23:41:05.481147image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:05.656957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:06.747103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:06.889456image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:07.047677image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:07.179274image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:07.307815image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:07.444034image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:07.581313image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:07.725167image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:07.857305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:07.996958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:08.120524image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:08.246241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:08.377548image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:08.511649image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:08.653751image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:08.793353image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:08.928456image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:09.050059image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:09.186643image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:09.315646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:09.448135image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:09.576640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:09.704369image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:09.849874image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:09.980527image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:10.124549image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:10.255038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:10.398322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:10.527480image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:10.677674image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:10.933626image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:11.068492image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:11.185495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:11.325319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:11.460769image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:11.606241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:11.745322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:11.885170image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:12.013294image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:12.138146image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:12.272596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:12.403622image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:12.536963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:12.676040image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:12.820799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:12.956847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:13.080898image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:13.223836image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:13.357146image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:13.501564image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:13.653151image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:13.800816image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:13.935259image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:14.083802image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:14.225940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:14.361617image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:14.500899image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:14.646605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:14.792917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:14.937325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:15.065007image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:15.196131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:15.336411image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:15.474085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:15.616227image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:15.738352image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:16.017755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:16.147739image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:16.268075image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:16.399724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:16.520505image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:16.653149image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:16.781861image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:16.921215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:17.041595image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:17.179593image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:17.305662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:17.433262image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:17.562322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:17.698691image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:17.833382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:17.966913image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:18.106293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:18.239077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:18.371537image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:18.496695image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:18.628802image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:18.764992image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:18.903980image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:19.038897image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:19.177303image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:19.328416image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:19.466491image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:19.626446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:19.780828image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:19.951923image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:20.095305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:20.249590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:20.386550image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:20.527070image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:20.673496image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:20.827905image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:20.977706image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:21.120140image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:21.262330image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:21.400130image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:21.550082image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:21.696159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:21.849367image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:22.181508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:22.302655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:22.430700image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:22.559204image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:22.691477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:22.812192image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:22.944613image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:23.069513image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:23.206345image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:23.333105image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:23.473464image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:23.595463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:23.721233image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:23.855478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:23.989602image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:24.127981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:24.265651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:24.390724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:24.513061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:24.649099image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:24.776539image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:24.909233image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:25.038826image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:25.175009image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:25.321029image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:25.464701image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:25.622749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:25.763642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:25.917798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:26.064111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:26.222213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:26.379432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:26.545811image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:26.686282image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:26.829717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:26.975545image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:27.119631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:27.268220image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:27.424694image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:27.568277image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:27.707729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:27.859723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:28.004063image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:28.201498image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:28.337357image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:28.472051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:28.609543image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:28.739239image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:28.890734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:29.015239image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:29.153249image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:29.292562image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:29.442108image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:29.847605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:29.997926image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:30.132547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:30.268413image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:30.408229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:30.554234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:30.700562image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:30.848571image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:30.981668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:31.110261image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:31.248677image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:31.387473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:31.531813image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:31.677333image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:31.823154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:31.976167image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:32.123355image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:32.282606image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:32.428980image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:32.593300image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:32.745968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:32.910604image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:33.051567image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:33.208635image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:33.356477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:33.505755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:33.664801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:33.833648image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:34.005036image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:34.179750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:34.334625image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:34.482630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:34.657248image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:34.830523image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:34.995180image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:35.122432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:35.246457image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:35.392468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:35.524313image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:35.668902image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:35.797431image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:35.940253image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:36.077398image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:36.224880image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:36.358466image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:36.503869image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:36.631457image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:36.775853image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:36.917522image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:37.056801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:37.194330image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:37.344474image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:37.478454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:37.611183image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:37.761814image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:37.896834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:38.041345image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:38.188744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:38.352739image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:38.511442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:38.661032image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:38.826382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:38.970412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:39.130094image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:39.625389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:39.806511image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:39.964104image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:40.131005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:40.279586image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:40.433947image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:40.593329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:40.754880image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:40.919682image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:41.071461image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:41.214653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:41.361694image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:41.522111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:41.677583image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:41.836962image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:41.960252image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:42.084042image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:42.214184image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:42.341626image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:42.477070image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:42.601063image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:42.736052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:42.881247image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:43.022175image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:43.149640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:43.283734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:43.405344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:43.530804image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:43.661385image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:43.794838image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:43.932212image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:44.066229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:44.198287image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:44.323065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:44.469404image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:44.602298image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:44.747040image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:44.880654image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:45.007064image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:45.136195image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:45.259918image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:45.405172image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:45.534608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:45.687733image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:45.842034image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:46.002472image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:46.145733image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:46.306040image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:46.448139image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:46.592354image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:46.733667image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:46.895790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:47.037286image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:47.171968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:47.310753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:47.444965image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:47.602753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:47.751042image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:47.905162image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:48.046345image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:48.188113image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:48.334957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:48.481797image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:48.639780image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:48.783813image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:48.947658image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:49.100108image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:49.272640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:49.432305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:49.609040image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:49.751258image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:49.907657image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:50.062496image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:50.227309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:50.384868image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:50.554633image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:50.707588image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:50.854416image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:51.017111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:51.173578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:51.768463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:51.915720image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:52.064906image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:52.214699image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:52.355170image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:52.512932image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:52.658832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:52.832664image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:52.996830image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:53.195660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:53.364263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:53.556636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:53.721177image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:53.898216image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:54.084338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:54.255269image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:54.429711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:54.606416image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:54.766651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:54.919427image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:55.085994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:55.254658image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:55.430276image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:55.595652image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:55.754036image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:55.930970image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:56.096235image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:56.280201image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:56.444631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:56.622242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:56.796169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:56.978830image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:57.149966image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:57.333107image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:57.500661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:57.675975image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:57.866380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:58.050146image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:58.244744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:58.422626image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:58.610981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:58.769216image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:58.949159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:59.114122image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:59.288560image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:59.448732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:59.631094image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:59.818678image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:41:59.976063image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:00.173638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:00.337249image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:00.518368image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:00.690079image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:00.866618image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:01.029727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:01.216325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:01.391727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:01.571332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:01.750769image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:01.940817image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:02.171633image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:02.362115image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:02.563511image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:02.741235image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:02.952037image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:03.188103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:03.428270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:03.627544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:03.802916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:03.980069image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:04.149380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:04.319498image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:04.470173image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:04.687226image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:04.887414image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:05.061377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:05.216357image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:05.384744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:05.547966image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:05.700941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:05.880184image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:06.043933image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:06.208297image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:06.377835image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:06.556129image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:06.701699image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:06.877251image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:07.037640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:07.193507image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:07.359510image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:07.502386image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:07.660509image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:07.811912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:07.989321image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:08.164005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:08.320391image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:10.154647image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:10.337181image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:10.529185image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:10.698407image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:10.868688image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:11.022039image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:11.206640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:11.387084image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:11.557420image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:11.728305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:11.893900image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:12.041997image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:12.209793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:12.360624image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:12.546543image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:12.743273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:12.924125image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:13.096674image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:13.260751image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:13.447120image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:13.613873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:13.796996image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:13.971744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:14.255938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:14.503920image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:14.786809image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:14.938188image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:15.105941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:15.282458image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:15.471767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:15.664894image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:15.864811image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:16.047579image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:16.233665image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:16.457190image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:16.632051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:16.813347image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:16.971689image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:17.127755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:17.308746image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:17.471895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:17.638765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:17.792797image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:17.960952image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:18.145514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:18.332844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:18.489044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:18.664902image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:18.813530image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:18.964410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:19.132288image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:19.297829image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:19.487210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:19.678471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:19.899224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:20.052450image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:20.233152image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:20.394269image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:20.564345image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:20.717534image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:20.902074image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:21.070850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:21.249752image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:21.426096image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:21.589751image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:21.772803image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:21.934493image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:22.131850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:22.296412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:22.482631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:22.656618image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:22.818724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:22.977362image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:23.157988image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:23.334680image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:23.512247image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:23.682853image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:23.841600image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:24.046635image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:24.257687image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-10-15T23:42:40.643524image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-15T23:42:41.127941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-15T23:42:41.600667image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-15T23:42:42.087485image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-10-15T23:42:24.845884image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:25.868636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:26.682185image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-15T23:42:27.898965image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

df_indexContinentCountryPopulation_DensityFertility_RateGDP_PPPCorruptionGovernment_EffectivenessPolitical_StabilityRule_of_LawGovernment_Healthcare_SpendUrban_PopulationSmoking_PrevalenceTourismWomen_In_ParliamentObesity_RateYearDiabetes Prevelance % of Population 20-79Labour Force TotalPopulationPopulation Aged 65 and above % of TotalUrban PopulationDeath Rate Per 1000 (2017)PM2.5 air pollution, mean annual exposure (micrograms per cubic meter) (2017)PM2.5 air pollution, population exposed to levels exceeding WHO guideline value (% of total) (2017)
00AsiaAfghanistan59.604.041800.0-1.496648-1.457285-2.746730-1.6684780.0509580.1080NaNNaN0.2786890.0552019.09.210699797.038041754.02.6157949797273.06.57556.910808100.000000
1282EuropeAlbania105.001.7013200.0-0.5218670.1147880.382100-0.392243NaNNaN0.2875340000.00.2950820.2172019.09.01313923.02854191.014.2026311747593.07.71418.200603100.000000
2495AfricaAlgeria18.402.5414000.0-0.636465-0.443925-0.793785-0.7753090.6595470.06340.1562657000.00.2575760.2742019.06.712303926.043053054.06.55277831510100.04.71738.884011100.000000
3777EuropeAndorra164.00NaN55000.01.2386141.9449761.4254321.6077380.490320NaN0.3353042000.00.4642860.2562019.07.7NaN77142.0NaN67873.0NaN10.30762117.824459
4991AfricaAngola26.405.415440.0-1.144541-1.052086-0.319035-1.0480230.4627860.2530NaN218000.00.3000000.0822019.04.513164276.031825295.02.19934221061025.08.43232.388505100.000000
51191North AmericaAntigua and Barbuda223.002.0125000.00.276635-0.0060100.7261430.3933260.470350NaNNaN269000.00.1111110.1892019.013.1NaN97118.09.05496423800.06.31618.622343100.000000
61393South AmericaArgentina16.502.2317100.0-0.0834680.0259880.019932-0.2400080.7243210.42900.2186942000.00.4015750.2832019.05.920772672.044938712.011.24310841339571.07.61613.31183493.852825
71610EuropeArmenia104.001.5910200.0-0.349113-0.024339-0.418780-0.1500260.1324470.36600.2411652000.00.2348490.2022019.06.11303565.02957731.011.4836681869848.09.86232.528118100.000000
81892OceaniaAustralia3.321.8145800.01.8062011.5964950.9765741.7152790.6891290.60900.1479246000.00.3046360.2902019.05.613414956.025364307.015.92120221844756.06.5008.55032424.893584
92174EuropeAustria109.001.5447300.01.5962621.4533420.9175231.8758340.7239250.21600.29630816000.00.3934430.2012019.06.64613292.08877067.019.0756065194416.09.50012.47796785.051538

Last rows

df_indexContinentCountryPopulation_DensityFertility_RateGDP_PPPCorruptionGovernment_EffectivenessPolitical_StabilityRule_of_LawGovernment_Healthcare_SpendUrban_PopulationSmoking_PrevalenceTourismWomen_In_ParliamentObesity_RateYearDiabetes Prevelance % of Population 20-79Labour Force TotalPopulationPopulation Aged 65 and above % of TotalUrban PopulationDeath Rate Per 1000 (2017)PM2.5 air pollution, mean annual exposure (micrograms per cubic meter) (2017)PM2.5 air pollution, population exposed to levels exceeding WHO guideline value (% of total) (2017)
17039449AsiaUnited Arab Emirates118.01.6965300.01.1516081.4312910.7432380.8064480.7197390.60500.28921286000.00.5000000.3172019.016.36840917.09770529.01.1565498479744.01.42940.917510100.000000
17139731EuropeUnited Kingdom281.01.8740700.01.8265771.3418840.0487181.6386370.7941180.27000.22336316000.00.3200000.2782019.03.934530463.066834405.018.50869655908316.09.20010.47269066.533249
17240013North AmericaUnited States36.21.8957500.01.3232181.5769980.4774611.4532550.5015810.46500.21879745920.00.2343390.3622019.010.8165890069.0328239523.016.209606270663028.08.6387.4094423.343170
17340295South AmericaUruguay19.81.9521300.01.2655340.5582151.0465090.5991070.7083480.50400.1683469000.00.1919190.2792019.07.31762838.03461734.014.9412403303394.09.4619.27488322.385745
17440502AsiaUzbekistan78.72.186790.0-1.068714-0.547083-0.283422-1.0736320.4329950.07420.1265346000.00.1600000.1662019.06.515555948.033580650.04.59521616935729.05.00028.455901100.000000
17540708South AmericaVenezuela32.22.239050.0-1.467512-1.581517-1.343512-2.3386220.1593870.3310NaNNaN0.2215570.2562019.07.012364879.028515829.07.61099025162368.06.81417.00855499.933662
17640915AsiaVietnam314.01.947350.0-0.485357-0.0034450.203266-0.0032820.4862290.17300.22815498000.00.2672070.0212019.06.057364650.096462106.07.55367335332140.06.25329.626728100.000000
17741197AsiaYemen56.53.612330.0-1.637971-2.244354-3.002496-1.790394NaN0.09860.184NaN0.0033220.1712019.05.46734980.029161922.02.90214110869523.05.97850.456007100.000000
17841378AfricaZambia24.74.763650.0-0.656137-0.5592090.141994-0.3449390.3863300.14800.1381072000.00.1796410.0812019.04.57401485.017861030.02.1153157871713.06.63327.438035100.000000
17941581AfricaZimbabwe38.43.472410.0-1.235616-1.198860-0.707054-1.2736300.5161580.10400.1582580000.00.3185180.1552019.01.87039493.014645468.02.9806084717305.08.04422.251671100.000000